Machine-Learning & Data-driven identification of variants to improve neonatal care and patient outcomes


This project exploits the population data held in the National Neonatal Research Database (NNRD), to develop and apply machine-learning (ML) techniques for the systematic identification of unwarranted variation in a set of clinical outcomes, and their principal care-related determinants.

This is the first time ML tools are applied to the NNRD cohort.

Our initial application has focused on discovery of feeding patterns for very pre-term babies and their association with hazard ratios on outcomes such as mortality, length of stay or breast-milk feeding at discharge.


Contact Group member(s):

Sam Greenbury, Jinyi Wu, Elsa Angelini



Contact Group member(s):

Paul Blakeley